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Found 4 papers, 3 papers with code
Analyzing the Effectiveness of the Underlying Reasoning Tasks in Multi-hop Question Answering
To explain the predicted answers and evaluate the reasoning abilities of models, several studies have utilized underlying reasoning (UR) tasks in multi-hop question answering (QA) datasets.
Multi-hop Question Answering
Open-Ended Question Answering
+1
Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps
The evidence information has two benefits: (i) providing a comprehensive explanation for predictions and (ii) evaluating the reasoning skills of a model.
mgsohrab at WNUT 2020 Shared Task-1: Neural Exhaustive Approach for Entity and Relation Recognition Over Wet Lab Protocols
In relation extraction task, we achieved 80. 46% in terms of F-score as the top system in the relation extraction or recognition task.
Ranked #1 on
Named Entity Recognition (NER)
on WNUT 2020
Coreference Resolution in Full Text Articles with BERT and Syntax-based Mention Filtering
Additionally, the proposed model is able to detect coreferent pairs in long distances, even with a distance of more than 200 sentences.
